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53. Vessel and Airway Characteristics in One-Year CT-defined Rapid Emphysema Progression: SPIROMICS

Author

Gerard, Sarah E, primary, Dougherty, Timothy M., additional, Nagpal, Prashant, additional, Jin, Dakai, additional, Han, MeiLan K., additional, Newell Jr., John D, additional, Saha, Punam K, additional, Comellas, Alejandro P., additional, Cooper, Christopher B., additional, Couper, David, additional, Fortis, Spyridon, additional, Guo, Junfeng, additional, Hansel, Nadia N, additional, Kanner, Richard E, additional, Kazeroni, Ella A, additional, Martinez, Fernando J., additional, Motahari, Amin, additional, Paine III, Robert, additional, Rennard, Stephen, additional, Schroeder, Joyce D., additional, Woodruff, Prescott G., additional, Barr, R Graham, additional, Smith, Benjamin M, additional, and Hoffman, Eric A., additional

Published
2024
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55. Vessel and Airway Characteristics in One-Year Computed Tomography–defined Rapid Emphysema Progression: SPIROMICS.

Author

Gerard, Sarah E., Dougherty, Timothy M., Nagpal, Prashant, Jin, Dakai, Han, MeiLan K., Newell Jr., John D., Saha, Punam K., Comellas, Alejandro P., Cooper, Christopher B., Couper, David, Fortis, Spyridon, Guo, Junfeng, Hansel, Nadia N., Kanner, Richard E., Kazeroni, Ella A., Martinez, Fernando J., Motahari, Amin, Paine III, Robert, Rennard, Stephen, and Schroeder, Joyce D.

Subjects
LUNGS, PULMONARY circulation, LUNG volume, CHRONIC obstructive pulmonary disease, OBSTRUCTIVE lung diseases, FORCED expiratory volume, VASCULAR resistance
Abstract

Rationale: Rates of emphysema progression vary in chronic obstructive pulmonary disease (COPD), and the relationships with vascular and airway pathophysiology remain unclear. Objectives: We sought to determine if indices of peripheral (segmental and beyond) pulmonary arterial dilation measured on computed tomography (CT) are associated with a 1-year index of emphysema (EI; percentage of voxels <−950 Hounsfield units) progression. Methods: Five hundred ninety-nine former and never-smokers (Global Initiative for Chronic Obstructive Lung Disease stages 0–3) were evaluated from the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort: rapid emphysema progressors (RPs; n = 188, 1-year ΔEI > 1%), nonprogressors (n = 301, 1-year ΔEI ± 0.5%), and never-smokers (n = 110). Segmental pulmonary arterial cross-sectional areas were standardized to associated airway luminal areas (segmental pulmonary artery–to–airway ratio [PAARseg]). Full-inspiratory CT scan–derived total (arteries and veins) pulmonary vascular volume (TPVV) was compared with small vessel volume (radius smaller than 0.75 mm). Ratios of airway to lung volume (an index of dysanapsis and COPD risk) were compared with ratios of TPVV to lung volume. Results: Compared with nonprogressors, RPs exhibited significantly larger PAARseg (0.73 ± 0.29 vs. 0.67 ± 0.23; P = 0.001), lower ratios of TPVV to lung volume (3.21 ± 0.42% vs. 3.48 ± 0.38%; P = 5.0 × 10−12), lower ratios of airway to lung volume (0.031 ± 0.003 vs. 0.034 ± 0.004; P = 6.1 × 10−13), and larger ratios of small vessel volume to TPVV (37.91 ± 4.26% vs. 35.53 ± 4.89%; P = 1.9 × 10−7). In adjusted analyses, an increment of 1 standard deviation in PAARseg was associated with a 98.4% higher rate of severe exacerbations (95% confidence interval, 29–206%; P = 0.002) and 79.3% higher odds of being in the RP group (95% confidence interval, 24–157%; P = 0.001). At 2-year follow-up, the CT-defined RP group demonstrated a significant decline in postbronchodilator percentage predicted forced expiratory volume in 1 second. Conclusions: Rapid one-year progression of emphysema was associated with indices indicative of higher peripheral pulmonary vascular resistance and a possible role played by pulmonary vascular–airway dysanapsis. [ABSTRACT FROM AUTHOR]

Published
2024
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56. List of contributors

Author

Alvarez, Jeffery B., primary, Bibault, Jean-Emmanuel, additional, Burgun, Anita, additional, Cai, Jinzheng, additional, Cao, Zhidong, additional, Chang, Ken, additional, Chen, Jonathan H., additional, Chen, William C., additional, Cho, Mildred, additional, Cho, Peter Jaeho, additional, Cornish, Toby C., additional, Costa, Anthony, additional, Dekker, Andre, additional, Drukker, Karen, additional, Dunn, Jessilyn, additional, Eminaga, Okyaz, additional, Erickson, Bradley J., additional, Fournier, Laure, additional, Gambhir, Sanjiv Sam, additional, Gennatas, Efstathios D., additional, Giger, Maryellen L., additional, Halilaj, Iva, additional, Harrison, Adam P., additional, He, Bryan, additional, Hong, Julian C., additional, Jin, Dakai, additional, Jin, Michael C., additional, Jochems, Arthur, additional, Kalpathy-Cramer, Jayashree, additional, Kapp, Daniel S., additional, Karimzadeh, Mehran, additional, Karnes, William, additional, Lambin, Philippe, additional, Langlotz, Curtis P., additional, Lee, Joonsang, additional, Li, Hui, additional, Liao, Joseph C., additional, Lin, Anthony L., additional, Lin, Rebecca Y., additional, Liu, Yun, additional, Lu, Le, additional, Magnus, David, additional, McIntosh, Chris, additional, Miao, Shun, additional, Min, James K., additional, Neill, Daniel B., additional, Oermann, Eric Karl, additional, Ouyang, David, additional, Peng, Lily, additional, Phene, Sonia, additional, Poirot, Maarten G., additional, Quon, Jennifer L., additional, Ranti, Daniel, additional, Rao, Arvind, additional, Raskar, Ramesh, additional, Rombaoa, Christopher, additional, Rubin, Daniel L., additional, Samarasena, Jason, additional, Seekins, Jayne, additional, Seetharam, Karthik, additional, Shearer, Emily, additional, Sibley, Adam, additional, Singh, Karnika, additional, Singh, Praveer, additional, Sordo, Margarita, additional, Suraweera, Duminda, additional, Valliani, Aly Al-Amyn, additional, van Wijk, Yvonka, additional, Vepakomma, Praneeth, additional, Wang, Bo, additional, Wang, Ge, additional, Wang, Nicholas, additional, Wang, Yirui, additional, Warner, Elisa, additional, Welch, Mattea, additional, Wong, Kimberly, additional, Wu, Zhenqin, additional, Xing, Fuyong, additional, Xing, Lei, additional, Yan, Ke, additional, Yan, Pingkun, additional, Yang, Lu, additional, Yeom, Kristen W., additional, Zachariah, Robin, additional, Zeng, Daniel, additional, Zhang, Lin, additional, Zhang, Ling, additional, Zhang, Xuhong, additional, Zhou, Li, additional, and Zou, James, additional

Published
2021
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58. Artificial intelligence in radiology

Author

Jin, Dakai, primary, Harrison, Adam P., additional, Zhang, Ling, additional, Yan, Ke, additional, Wang, Yirui, additional, Cai, Jinzheng, additional, Miao, Shun, additional, and Lu, Le, additional

Published
2021
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60. 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels

Author

Jin, Dakai, Xu, Ziyue, Harrison, Adam P., George, Kevin, Mollura, Daniel J., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Qian, editor, Shi, Yinghuan, editor, Suk, Heung-Il, editor, and Suzuki, Kenji, editor

Published
2017
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61. Segmentation of Trabecular Bone for In Vivo CT Imaging Using a Novel Approach of Computing Spatial Variation in Bone and Marrow Intensities

Author

Chen, Cheng, Jin, Dakai, Zhang, Xiaoliu, Levy, Steven M., Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Chu-Song, editor, Lu, Jiwen, editor, and Ma, Kai-Kuang, editor

Published
2017
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62. A Novel Iterative Method for Airway Tree Segmentation from CT Imaging Using Multiscale Leakage Detection

Author

Nadeem, Syed Ahmed, Jin, Dakai, Hoffman, Eric A., Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Chu-Song, editor, Lu, Jiwen, editor, and Ma, Kai-Kuang, editor

Published
2017
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67. Fuzzy Skeletonization Improves the Performance of Characterizing Trabecular Bone Micro-architecture

Author

Chen, Cheng, Jin, Dakai, Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Pavlidis, Ioannis, editor, Feris, Rogerio, editor, McGraw, Tim, editor, Elendt, Mark, editor, Kopper, Regis, editor, Ragan, Eric, editor, Ye, Zhao, editor, and Weber, Gunther, editor

Published
2015
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68. Filtering Non-Significant Quench Points Using Collision Impact in Grassfire Propagation

Author

Jin, Dakai, Chen, Cheng, Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Murino, Vittorio, editor, and Puppo, Enrico, editor

Published
2015
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69. Deep Implicit Statistical Shape Models for 3D Medical Image Delineation

Author

Raju, Ashwin, Miao, Shun, Jin, Dakai, Lu, Le, Huang, Junzhou, and Harrison, Adam P.

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, General Medicine
Abstract

3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Today fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process(MDP). We outline a training regime that includes inverted episodic training and a deep realization of marginal space learning (MSL). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than three leading FCN models, including nnU-Net: reducing the mean Hausdorff distance (HD) by 7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on a dataset directly reflecting clinical deployment scenarios demonstrate that DISSMs improve the mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case DSC by 5.4-7.3%. These improvements are over and above any benefits from representing delineations with high-quality surface.

Published
2022
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73. Volumetric Topological Analysis on In Vivo Trabecular Bone Magnetic Resonance Imaging

Author

Chen, Cheng, Jin, Dakai, Liu, Yinxiao, Wehrli, Felix W., Chang, Gregory, Snyder, Peter J., Regatte, Ravinder R., Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, McMahan, Ryan, editor, Jerald, Jason, editor, Zhang, Hui, editor, Drucker, Steven M., editor, Kambhamettu, Chandra, editor, El Choubassi, Maha, editor, Deng, Zhigang, editor, and Carlson, Mark, editor

Published
2014
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74. Automated Assessment of Pulmonary Arterial Morphology in Multi-row Detector CT Imaging Using Correspondence with Anatomic Airway Branches

Author

Jin, Dakai, Iyer, Krishna S., Hoffman, Eric A., Saha, Punam K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, McMahan, Ryan, editor, Jerald, Jason, editor, Zhang, Hui, editor, Drucker, Steven M., editor, Kambhamettu, Chandra, editor, El Choubassi, Maha, editor, Deng, Zhigang, editor, and Carlson, Mark, editor

Published
2014
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75. A New Algorithm for Cortical Bone Segmentation with Its Validation and Applications to In Vivo Imaging

Author

Li, Cheng, Jin, Dakai, Burns, Trudy L., Torner, James C., Levy, Steven M., Saha, Punam K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Petrosino, Alfredo, editor

Published
2013
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76. A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging

Author

Jin, Dakai, Saha, Punam K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Petrosino, Alfredo, editor

Published
2013
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79. LViT: Language Meets Vision Transformer in Medical Image Segmentation

Author

Li, Zihan, Li, Yunxiang, Li, Qingde, Wang, Puyang, Guo, Dazhou, Lu, Le, Jin, Dakai, Zhang, You, and Hong, Qingqi

Abstract

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

Published
2024
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84. Contributors

Author

Bai, Xiang, primary, Bengtsson, Ewert, additional, Bertrand, Gilles, additional, Bloch, Isabelle, additional, Borgefors, Gunilla, additional, Chen, Cheng, additional, Couprie, Michel, additional, Fagertun, Jens, additional, Falcão, Alexandre, additional, Feng, Cong, additional, Frimmel, Hans, additional, Gil, Debora, additional, González Ballester, Miguel Á., additional, Hoffman, Eric A., additional, Jalba, Andrei, additional, Jin, Dakai, additional, Kjer, H.M., additional, Koehoorn, Joost, additional, Kong, T.Y., additional, Kustra, Jacek, additional, Lidayová, Kristína, additional, Paulsen, Rasmus R., additional, Saha, Punam K., additional, Sanniti di Baja, Gabriella, additional, Serino, Luca, additional, Shen, Wei, additional, Smedby, Örjan, additional, Telea, Alexandru, additional, Vera, Sergio, additional, Wang, Chunliang, additional, Wang, Yan, additional, Yuan, Jiang, additional, Zhang, Zhijiang, additional, and Zhao, Kai, additional

Published
2017
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89. Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT

Author

Ye, Xianghua, Guo, Dazhou, Tseng, Chen-Kan, Ge, Jia, Hung, Tsung-Min, Pai, Ping-Ching, Ren, Yanping, Zheng, Lu, Zhu, Xinli, Peng, Ling, Chen, Ying, Chen, Xiaohua, Chou, Chen-Yu, Chen, Danni, Yu, Jiaze, Chen, Yuzhen, Jiao, Feiran, Xin, Yi, Huang, Lingyun, Xie, Guotong, Xiao, Jing, Lu, Le, Yan, Senxiang, Jin, Dakai, and Ho, Tsung-Ying

Subjects
FOS: Computer and information sciences, Cancer Research, delineation, Computer Vision and Pattern Recognition (cs.CV), segmentation, Computer Science - Computer Vision and Pattern Recognition, deep learning, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Oncology, esophageal cancer, PET/CT (18)F-FDG, radiotherapy, RC254-282, Original Research
Abstract

Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To validate the clinical applicability of a deep learning (DL) multi-modality esophageal GTV contouring model, developed at 1 institution whereas tested at multiple ones. Methods and Materials: We collected 606 esophageal cancer patients from four institutions. 252 institution-1 patients had a treatment planning-CT (pCT) and a pair of diagnostic FDG-PETCT; 354 patients from other 3 institutions had only pCT. A two-streamed DL model for GTV segmentation was developed using pCT and PETCT scans of a 148 patient institution-1 subset. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PETCT combined. For independent evaluation, the rest 104 institution-1 patients behaved as unseen internal testing, and 354 institutions 2-4 patients were used for external testing. We evaluated manual revision degrees by human experts to assess the contour-editing effort. The performance of the deep model was compared against 4 radiation oncologists in a multiuser study with 20 random external patients. Contouring accuracy and time were recorded for the pre-and post-DL assisted delineation process. Results: Our model achieved high segmentation accuracy in internal testing (mean Dice score: 0.81 using pCT and 0.83 using pCT+PET) and generalized well to external evaluation (mean DSC: 0.80). Expert assessment showed that the predicted contours of 88% patients need only minor or no revision. In multi-user evaluation, with the assistance of a deep model, inter-observer variation and required contouring time were reduced by 37.6% and 48.0%, respectively. Conclusions: Deep learning predicted GTV contours were in close agreement with the ground truth and could be adopted clinically with mostly minor or no changes., 36 pages, 10 figures

Published
2022
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94. Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study

Author

Guo, Dazhou, primary, Ge, Jia, additional, Ye, Xianghua, additional, Yan, Senxiang, additional, Xin, Yi, additional, Song, Yuchen, additional, Huang, Bing-shen, additional, Hung, Tsung-Min, additional, Zhu, Zhuotun, additional, Peng, Ling, additional, Ren, Yanping, additional, Liu, Rui, additional, Zhang, Gong, additional, Mao, Mengyuan, additional, Chen, Xiaohua, additional, Lu, Zhongjie, additional, Li, Wenxiang, additional, Yuzhen, Chen, additional, Huang, Lingyun, additional, Xiao, Jing, additional, Harrison, Adam, additional, Lu, Le, additional, Lin, Chien-Yu, additional, Jin, Dakai, additional, and Ho, Tsung-Ying, additional

Published
2021
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96. CT-Based Risk Factors for Mortality of Patients With COVID-19 Pneumonia in Wuhan, China: A Retrospective Study

Author

Li, Xiang, primary, Li, Nannan, additional, Chen, Zhen, additional, Ye, Ling, additional, Zhang, Ling, additional, Jin, Dakai, additional, Gao, Liangxin, additional, Liu, Xinhui, additional, Lai, Bolin, additional, Yao, Jiawen, additional, Guo, Dazhou, additional, Zhang, Hua, additional, Lu, Le, additional, Xiao, Jing, additional, Huang, Lingyun, additional, Ai, Fen, additional, and Wang, Xiang, additional

Published
2021
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